# tf.nn.ctc_beam_search_decoder

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/ctc_ops.py#L445-L494) |

Performs beam search decoding on the logits given in input.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.ctc_beam_search_decoder_v2`](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_beam_search_decoder)

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    tf.nn.ctc_beam_search_decoder(
        inputs, sequence_length, beam_width=100, top_paths=1
    )

> [!NOTE]
> **Note:** Although in general greedy search is a special case of beam-search with `top_paths=1` and `beam_width=1`, `ctc_beam_search_decoder` differs from `ctc_greedy_decoder` in the treatment of blanks when computing the probability of a sequence:

- `ctc_beam_search_decoder` treats blanks as sequence termination
- `ctc_greedy_decoder` treats blanks as regular elements

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| ## Args ||
|---|---|
| `inputs` | 3-D `float` `Tensor`, size `[max_time, batch_size, num_classes]`. The logits. |
| `sequence_length` | 1-D `int32` vector containing sequence lengths, having size `[batch_size]`. |
| `beam_width` | An int scalar \>= 0 (beam search beam width). |
| `top_paths` | An int scalar \>= 0, \<= beam_width (controls output size). |

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| ## Returns ||
|---|---|
| A tuple `(decoded, log_probabilities)` where ||
| `decoded` | A list of length top_paths, where `decoded[j]` is a `SparseTensor` containing the decoded outputs: <br /> `decoded[j].indices`: Indices matrix `[total_decoded_outputs[j], 2]`; The rows store: `[batch, time]`. `decoded[j].values`: Values vector, size `[total_decoded_outputs[j]]`. The vector stores the decoded classes for beam `j`. `decoded[j].dense_shape`: Shape vector, size `(2)`. The shape values are: `[batch_size, max_decoded_length[j]]`. |
| `log_probability` | A `float` matrix `[batch_size, top_paths]` containing sequence log-probabilities. |

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